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adirik/mamba-2.8b

A state-space model (SSM) based language model utilizing the innovative Mamba architecture with 2.8 billion parameters. Unlike traditional transformer-based models, Mamba-2.8b employs selective state space modeling for efficient sequence processing with linear scaling characteristics with respect to sequence length. This model demonstrates competitive performance compared to transformer models of similar size while offering significant computational advantages including reduced memory requirements and faster inference at longer sequence lengths.

Technical Specifications:

  • Architecture: Selective State Space Model (Mamba)
  • Parameters: 2.8 billion
  • Sequence Handling: Linear computational scaling with sequence length
  • Memory Efficiency: Reduced memory footprint compared to transformers
  • Performance Focus: Long-context modeling with efficient computation
  • Applications: Long-form content analysis, extended context applications, efficiency-focused deployments
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